Trial and Error Replaces Human Teachers for Weaker AI Models
TL;DR
Artificial intelligence has undergone a profound shift in how it learns, moving from rigid teacher-driven methods to more flexible, student-driven approaches. This evolution has been particularly impactful for training weaker models, which often struggle with traditional methods like supervised fine-tuning.
Nauti's Take
For a small team, the first test is straightforward: train a weaker model on the same task once with supervised fine-tuning and once with GRPO or hint-based learning, then compare task success, token usage, and human correction time. Before switching pipelines, verify the evaluator itself, because trial-and-error training will optimize the signal your setup provides, including any shortcut or grading error.
Video
Sources
30.10.26
30.10.26